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Adaptation to Climate Change Adaptation to Climate Change Impact in China Impact in China - A Case Study on Flood Damage for Investment Decision-making March 2001 Introduction Objectives Model Structure Scenarios Analysis


  1. Adaptation to Climate Change Adaptation to Climate Change Impact in China Impact in China - A Case Study on Flood Damage for Investment Decision-making March 2001

  2. � Introduction � Objectives � Model Structure � Scenarios � Analysis � Conclusion

  3. INTRODUCTION INTRODUCTION � Change in flood risk is frequently cited as one of the potential effects of climate change. However, there have still been relatively few studies on that topic, though there are indications that the frequency of heavy rainfall events is generally likely to increase in some regions with global warming.

  4. Continued... � Attempts have been made to quantify changes in flood occurrence over small areas or from catchment basins. General conclusions are that both the frequency and intensity of floods may increase under changing climate in some seasons from some GCM output and that flood occurrence probability may double by the end of the next century.

  5. Continued... Continued... � The efficiency of each adaptation strategy has not been sufficiently analyzed quantitatively to propose a detailed action plan, main limitations are derived from the following features of climate change impact and adaptation studies : • Mechanisms of adaptation are too complex to be evaluated. • Climate change impact is still uncertain, and most impacts, even if they occur, will not be significant before the end of the 21 st century;

  6. OBJECTIVE OBJECTIVE � Introduce a model that adopts the standard approach of modern optimal economic growth theory and includes two discount factors from the climate sector, i.e., flood damages from climate variability and climate change. � Use this model to evaluate the benefits from investment as a robust adaptation strategy in flood prevention infrastructure to adapt to projected climate change impact in China.

  7. MODEL STRUCTURE MODEL STRUCTURE � Objective Function � Objective Function   − ∑ ∏   v = × + ρ t i Max U ( C ( t ) ) ( 1 )   i   t i The fundamental assumption is that policies should be designed to maximize the generalized level of consumption now and in the future. U is the flow of utility, C i ( t ) is the flow of consumption per capita at year t , ρ is the pure rate of social time preference, i is economic sectors, ν is consumption share of each sector product.

  8. � Production Function � Production Function   Maa ( t ) Mna ( t ) YGDPa ( t ) =   ( ) min , , Ya t   a 2 a n 2 a a 2 ao   Mnn ( t ) Man ( t ) YGDPn ( t ) =   ( ) min , , Yn t   n 2 n a 2 n n 2 no Ya ( t ) and Yn ( t ): the gross outputs of the agricultural and the non-agricultural sectors, respectively; Maa ( t ) and Man ( t ): intermediate inputs from the agricultural sector to both the agricultural and the non-agricultural sectors, respectively; Mnn ( t ) and Mna ( t ) are intermediate inputs from the non-agricultural sector to both the non-agricultural and the agricultural sectors, respectively. YGDPa ( t ) and YGDPn ( t ) are the productions of the agricultural and non-agricultural sectors. a2a , n2a , n2n , and a2n are input coefficients, and a2ao and n2no are production factors.

  9. Continued... Continued... β γ = × × × λ ( ) ( ) ( ) ( ) ( ) YGDPa t Aa t Ka t La t F t Aa(t) : total factor of productivity in agricultural sector at year t, : capital input to agricultural sector, Ka(t) La(t) : labor input to agricultural sector, : land input to agricultural sector, F(t) β : elasticity of capital input in agricultural sector, γ : elasticity of labor in agricultural sector, λ : elasticity of farmland in agricultural sector.

  10. Continued... Continued... − α α = × 1 × ( ) ( ) ( ) ( ) YGDPn t An t Kn t Ln t α : elasticity of output with respect to capital, An(t) : total factor of productivity in non-agricultural sector, Kn(t) : capital input at year t to agricultural sector, Ln(t) : labor input at year t to agricultural sector.

  11. � Material Balance Constraint � Material Balance Constraint = + + + + + ( ) ( ) ( ) ( ) ( ) ( ) ( ) Ya t Ca t Ia t IAa t IADa t Maa t Man t = + + + + + ( ) ( ) ( ) ( ) ( ) ( ) ( ) Yn t Cn t In t IAn t IADn t Mnn t Mna t + + + ( ) ( ) ( ) Iex t IAex t IADex t Ia ( t ), IAa ( t ) and IADa ( t ) are contributions of the agricultural sector to capital stock, investment for flood control, and extra investment for projected flood damage from climate change at year t . In ( t ), IAn ( t ) and IADn ( t ) are contributions of the non-agricultural sector to capital stock, investment for flood control, and extra investment for projected flood damage from climate change at year t . Iex ( t ) , IAex ( t ) and IADex ( t ) are contributions of the non-agricultural sector to agricultural sector in capital stock, investment for flood control, and extra investment for projected flood damage from climate change at year t . Ca ( t ) and Cn ( t ) are the consumptions of agricultural and non-agricultural goods, respectively.

  12. � Capital Constraints � Capital Constraints = − δ − + − + − ( ) ( 1 ) ( 1 ) ( 1 ) ( 1 ) Ka t Ka t Ia t Iex t = − δ − + − Kn ( t ) ( 1 ) Kn ( t 1 ) In ( t 1 ) = − + − + − + − ( ) ( ' ) ( ' ) ( ' ) ( ' ) INR t INR t t IAa t t IAn t t IAex t t = − + − + − + − ( ) ( ' ) ( ' ) ( ' ) ( ' ) INRA t INRA t t IADa t t IADn t t IADex t t δ is the depreciation rate; Ka ( t ) and Kn ( t ) are capital stocks of the agricultural and the non-agricultural sectors, respectively, at year t ; INR ( t ), INRA ( t ) are investments to infrastructure which prevent flooding from current climate variability and projected climate change at year t . t' is the time lag of investment taking effect.

  13. � Damage Function � Damage Function- -Climate Variability Climate Variability [ ] a 1 b 1 = − DAMak ( t ) 10 INR ( t ) / P ( t t ' ) [ ] 2 = a 2 − b ( ) 10 ( ) / ( ' ) DAMal t INR t P t t [ ] 3 3 = a − b DAMn ( t ) 10 INR ( t ) / P ( t t ' ) DAMn ( t ), DAMak ( t ), DAMal ( t ) are damages to capital stocks of the non-agricultural and agricultural sectors, and land at year t, respectively. P ( t ) is population at year t . a1, a2, a3, b1, b2, b3 are constants that are equivalent to 1.51273, 0.79413, 0.983978, -091843, -077078, -0.35482, respectively.

  14. � Damage Function � Damage Function - - Climate Change Climate Change

  15. � Damage Function � Damage Function - - Climate Change Climate Change ( ) b 1   1 / b 1 = a1 − + a 1 DAMalc(t) 10 ( ) / ( ' ) ( ) / 10 INRA t P t t Dc t     ( ) b 2   1 / 2 b = a2 − + a 2 DAMakc(t) 10 INRA ( t ) / P ( t t ' ) Dc ( t ) / 10     ( ) 3 b   1 / b 3 a3 3 = − + a DAMnc(t) 10 ( ) / ( ' ) ( ) / 10 INRA t P t t Dc t     DAMnc ( t ), DAMakc ( t ), DAMacl ( t ) are flood damages from climate change to capital stocks of the non-agricultural and the agricultural sectosr, and land at year t, respectively.

  16. � Annual climate change damage � Annual climate change damage 2 = × ( ) ( ) 6 . 25 Dc t Dref Tc t T ( t ) is the temperature increase in year t . Damage caused by flooding under the climate change of a 2.5 o C temperature increase is assumed to be Dref , the quadratic term of temperature reflects the assumption that the damage is quadratic along with temperature increase.

  17. � Growth Rate of Technology and Total � Growth Rate of Technology and Total Productivity Factor Productivity Factor [ ] − = × + ϕ t ( ) 0 1 ( ) GTa t GTa a t [ ] − = × + ϕ t ( ) 0 1 ( ) GTn t GTn n t [ ] = × ( ) 0 exp ( ) ALa t ALa GTa t [ ] = × ALn ( t ) ALn 0 exp GTn ( t ) φ a(t) and φ n(t) are the change rates of technology growth for the agricultural and the non-agricultural sectors, GTa(t), GTn(t), GTa0 and GTn0 are the growth rates of technolgoy of both sectors at year t and the initial year. ALa(t), ALn(t), ALa0, ALn0 are total productivity factors of agricultural and non-agricultural sector at year t and initial year

  18. SCENARIOS SCENARIOS - Climate Change And Investment Climate Change And Investment - Climate Change No Yes No CnAn CyAn Investment Yes CnAy CyAy

  19. SCENARIOS SCENARIOS - Population Growth In China Population Growth In China - Fertility rate Population in Population in (‰) 2000 (billion) 2100 (billion) Low scenario 1.62 1.26 0.8 Medium scenario 1.8 1.26 1.033 High scenario 2.1 1.26 1.5

  20. SCENARIOS SCENARIOS - Labor Employment In Labor Employment In - Different Sectors Different Sectors S cenario I II Year 2050 2100 2050 2100 Agri. S ector 20% 10% 30% 20% Non-Agri. S ector 80% 90% 70% 80% 0.585 0.2 0.404 0.2 Labor move rate (%) (1995-2050) (2051-2100) (1995-2050) (2051-2100)

  21. % 0.5 1.5 2.5 3.5 0 1 2 3 1995 2000 Flood damage to cultivated land 2005 BENEFIT ANALYSIS 2010 2015 2020 2025 2030 CnAy CyAy CyAn CnAn 2035 2040 2045 Year 2050 2055 2060 2065 2070 2075 2080 2085 2090 2095 2100

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